Application of combined probabilistic control in gas turbine tuning for power output-emissions parameters with scaling factor, related control systems, computer program products and methods

ABSTRACT

Various embodiments include a system having: at least one computing device configured to tune a set of gas turbines (GTs) by performing actions including: modelling each GT in the set of GTs at a base load level, based upon a measured ambient condition for each GT; commanding each GT in the set of GTs to adjust a respective power output to match a scaled power output value equal to a fraction of a difference between the respective power output and a nominal power output value, and measuring an actual emissions value for each GT during the adjusting of the respective power output; and adjusting an operating condition of each GT in the set of GTs based upon a difference between the respective measured actual emissions value, a nominal emissions value at the ambient condition and a nominal emissions value at the ambient condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application relates to co-pending U.S. patent application Ser. No.14/546,491, U.S. patent application Ser. No. 14/546,498, U.S. patentapplication Ser. No. 14/546,504, U.S. patent application Ser. No.14/546,512, U.S. patent application Ser. No. 14/546,520, and U.S. patentapplication Ser. No. 14/546,525. This application further relates toco-pending U.S. patent application Ser. No. 14/686,126, U.S. patentapplication Ser. No. 14/686,136, U.S. patent application Ser. No.14/686,145, U.S. patent application Ser. No. 14/686,151, U.S. patentapplication Ser. No. 14/686,157, U.S. patent application Ser. No.14/686,164, U.S. patent application Ser. No. 14/686,171, U.S. patentapplication Ser. No. 14/686,183, U.S. patent application Ser. No.14/686,193 and U.S. patent application Ser. No. 14/686,201; as well asU.S. patent application Ser. No. 14/960,930, U.S. patent applicationSer. No. 14/971,680, U.S. patent application Ser. No. 14/971,690, U.S.patent application Ser. No. 14/971,710, U.S. patent application Ser. No.14/971,716, U.S. patent application Ser. No. 14/971,721, U.S. patentapplication Ser. No. 14/971,736, and U.S. patent application Ser. No.14/971,740.

FIELD OF THE INVENTION

The subject matter disclosed herein relates to tuning and controlsystems. More particularly, the subject matter disclosed herein relatesto tuning and control systems for gas turbines.

BACKGROUND OF THE INVENTION

At least some known gas turbine engines include controllers that monitorand control their operation. Known controllers govern the combustionsystem of the gas turbine engine and other operational aspects of thegas turbine engine using operating parameters of the engine. At leastsome known controllers receive operating parameters that indicate thegas turbine engine's present operating state, define operationalboundaries by way of physics-based models or transfer functions, andapply the operating parameters to the operational boundary models.Additionally, at least some known controllers also apply the operatingparameters to scheduling algorithms, determining error terms, andcontrolling boundaries by adjusting one or more gas turbine enginecontrol effectors. However, at least some operating parameters may beunmeasured parameters, such as parameters that may be impractical tomeasure using sensors. Some of such parameters include firingtemperature (i.e., stage 1 turbine vane exit temperature), combustorexit temperature, and/or turbine stage 1 nozzle inlet temperature.

At least some known gas turbine engine control systems indirectlycontrol or monitor unmeasured operating parameters using measuredparameters, such as compressor inlet pressure and temperature,compressor exit pressure and temperature, turbine exhaust pressure andtemperature, fuel flow and temperature, ambient conditions, and/orgenerator power. However, there is uncertainty in the values of indirectparameters, and the associated gas turbine engines may need tuning toreduce combustion dynamics and emissions. Because of the uncertainty ofunmeasured parameters, design margins are used for gas turbine enginesthat include such known control systems. Using such design margins mayreduce the performance of the gas turbine engine at many operatingconditions in an effort to protect against and accommodate worst-caseoperational boundaries. Moreover, many of such known control systems maynot accurately estimate firing temperature or exhaust temperature of thegas turbine engine, which may result in a less efficient engine andvariation from machine-to-machine in facilities with more than one gasturbine engine.

It has proven difficult to reduce variation in firing temperature frommachine-to-machine for industrial gas turbines. For example, firingtemperature is a function of many different variables, includingvariations in the components of the gas turbine and their assembly.These variations are due to necessary tolerances in manufacturing,installation, and assembly of the gas turbine parts. In addition, thecontrols and sensors used to measure the operating parameters of the gasturbine contain a certain amount of uncertainty in their measurements.It is the uncertainty in the measurement system used to sense the valuesof the measured operating parameters and the machine componentvariations that necessarily result in variation of the unmeasuredoperating parameters of the gas turbine engine, such as the firingtemperature. The combination of these inherent inaccuracies makes itdifficult to achieve the design firing temperature of a gas turbineengine at a known set of ambient conditions and results in firingtemperature variation from machine-to-machine.

BRIEF DESCRIPTION OF THE INVENTION

Various embodiments include a system having: at least one computingdevice configured to tune a set of gas turbines (GTs) by performingactions including: modelling each GT in the set of GTs at a base loadlevel, based upon a measured ambient condition for each GT; commandingeach GT in the set of GTs to adjust a respective power output (mega-watt(MW) power output) to match a scaled power output value equal to afraction of a difference between the respective power output and anominal power output value, and measuring an actual emissions value foreach GT during the adjusting of the respective power output; andadjusting an operating condition of each GT in the set of GTs based upona difference between the respective measured actual emissions value, anominal emissions value at the ambient condition and an emissions scalefactor.

A first aspect includes a system having: at least one computing deviceconfigured to tune a set of gas turbines (GTs) by performing actionsincluding: modelling each GT in the set of GTs at a base load level,based upon a measured ambient condition for each GT; commanding each GTin the set of GTs to adjust a respective power output (MW power output)to match a scaled power output value equal to a fraction of a differencebetween the respective power output and a nominal power output value,and measuring an actual emissions value for each GT during the adjustingof the respective power output; and adjusting an operating condition ofeach GT in the set of GTs based upon a difference between the respectivemeasured actual emissions value, a nominal emissions value at theambient condition and an emissions scale factor.

A second aspect includes a computer program product having program code,which when executed by at least one computing device, causes the atleast one computing device to tune a set of gas turbines (GTs) byperforming actions including: modelling each GT in the set of GTs at abase load level, based upon a measured ambient condition for each GT;commanding each GT in the set of GTs to adjust a respective power output(MW power output) to match a scaled power output value equal to afraction of a difference between the respective power output and anominal power output value, and measuring an actual emissions value foreach GT during the adjusting of the respective power output; andadjusting an operating condition of each GT in the set of GTs based upona difference between the respective measured actual emissions value, anominal emissions value at the ambient condition and an emissions scalefactor.

A third aspect includes a computer-implemented method of tuning a set ofgas turbines (GTs), performed using at least one computing device, themethod including: modelling each GT in the set of GTs at a base loadlevel, based upon a measured ambient condition for each GT; commandingeach GT in the set of GTs to adjust a respective power output (MW poweroutput) to match a scaled power output value equal to a fraction of adifference between the respective power output and a nominal poweroutput value, and measuring an actual emissions value for each GT duringthe adjusting of the respective power output; and adjusting an operatingcondition of each GT in the set of GTs based upon a difference betweenthe respective measured actual emissions value, a nominal emissionsvalue at the ambient condition and an emissions scale factor.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings that depict various embodiments of the invention, in which:

FIG. 1 shows a schematic illustration of a gas turbine engine (GT),including a control system, according to various embodiments of theinvention.

FIG. 2 shows a schematic view of a control architecture that may be usedwith the control system of FIG. 1 to control operation of the GT,according to various embodiments of the invention.

FIG. 3 shows a graphical depiction of a probabilistic simulation of theoperating states of a statistically significant number of GT engines ofFIG. 1 using a model of the GT used by the control system of FIG. 1.

FIG. 4 shows a flow diagram illustrating a method according to variousembodiments of the invention.

FIG. 5 shows a graphical depiction of a process illustrated in the flowdiagram of FIG. 4, in a two-dimensional Power output (MW) v. Emissions(NO_(x)) graph.

FIG. 6 shows a graphical depiction of a process illustrated in the flowdiagram of FIG. 4, in a two-dimensional Power output (MW) v. Emissions(NO_(x)) graph.

FIG. 7 shows a graphical depiction of a process illustrated in the flowdiagram of FIG. 4, in a three-dimensional Power output (MW) v. Emissions(NO_(x)) v. firing temperature (T4) graph.

FIG. 8 shows an example graphical depiction of the adjustment of a gasturbine in an Error Signal v. Power Output (MW) graph according tovarious embodiments of the invention.

FIG. 9 shows an illustrative environment including a control systemaccording to various embodiments of the invention.

It is noted that the drawings of the invention are not necessarily toscale. The drawings are intended to depict only typical aspects of theinvention, and therefore should not be considered as limiting the scopeof the invention. In the drawings, like numbering represents likeelements between the drawings.

DETAILED DESCRIPTION OF THE INVENTION

As indicated above, subject matter disclosed herein relates to tuningand control systems. More particularly, the subject matter disclosedherein relates to tuning and control systems for gas turbines.

Probabilistic control is a methodology for setting the operating stateof a gas turbine (GT) based upon measured output (in mega-watts, MW) andmono-nitrogen oxides NO and NO₂ (nitric oxide and nitrogen dioxide),collectively referred to as NO_(x) emissions. As described herein,various embodiments provide tuning and control of a GT where errors inmeasurements exist. Conventional approaches exist to calculate and tunecontrol mechanisms where measurement errors exist, but no conventionalapproaches are designed to account for and tune GT control functions inspecific view of power output and NO_(x) measurements.

As used herein, term P50 GT or P50 machine refers to a mean (or,nominal) gas turbine or similar machine in a fleet. Parametersassociated with this P50 measure are considered ideal, and are rarely ifever attained in an actual gas turbine. Other terms used herein caninclude: a) firing temperature (T4), which is the average temperaturedownstream of a first-stage nozzle, but upstream of the first rotatingbucket in the turbine (e.g., GT); and b) T3.9, which is the combustiontemperature in the gas turbine, and is higher than the firingtemperature. The firing temperature, as is known in the art, cannot bemeasured, but is inferred from other measurements and known parameters.As used herein, the term, “indicated firing temperature” refers to thefiring temperature as indicated by one or more components of controlequipment, e.g., a control system monitoring and/or controlling GTcomponents. The “indicated” firing temperature represents the bestestimate of the firing temperature from conventional sensing/testingequipment connected with the GT control system.

Additionally, as described herein, the term “base load” for a particulargas turbine can refer to the maximum output of the gas turbine at ratedfiring temperature. Further, as described herein, and known in the art,base load for a given gas turbine will change based upon changes inambient operating conditions. Sometimes base load is referred to as“Full Speed Full Load” in the art. Further, it is understood that NOx issensitive to fuel composition, and as such, it is accounted for in anytuning processes conducted in a gas turbine (including tuning processesdescribed herein).

Further, as described herein, the term “exhaust energy” refers to theenergy contained within the exhaust gas exiting the GT, which may bedetermined based upon temperature measurements and pressure measurementsof the exhaust gas at the exhaust section (outlet) of the GT. Thisexhaust energy is directly related to the amount of combustion gasflowing through the GT, and can be correlated with other operatingparameters, e.g., power output.

Various embodiments described herein allow for probabilistic control ofGTs (e.g., a fleet of two or more GTs) using power output and emissionsparameters for the GTs. According to various embodiments, an approachcan include the following processes:

1) Modelling one or more gas turbines (e.g., in a fleet) at a designedbase load (MW value, NO_(x) value, fuel flow value, exhaust energyvalue), based upon a measured ambient condition. The modelling processcan include obtaining a model representative of the one or more gasturbines (e.g., a statistical model, a measurement-based model, etc.),and using the measured ambient condition as an input, simulating(modeling) the behavior of the gas turbine(s) as it (they) approach thedesigned base load. As described herein, in an ideal situation, theGT(s) should, converge to P50 (nominal) operating parameters, includinga P50 power output (nominal power output) value and P50 NO_(x)(emissions) value. However, as indicated herein, this does not occur inreal-world operations. The model will reflect this real-world operation;

2) Commanding the one or more GTs to adjust its power output (MW) tomatch a scaled power output value equal to a fraction of a differencebetween the respective power output (MW) and the nominal power output(P50 power output) value, and during that adjustment, measuring theactual NO_(x) value. Measuring the actual NO_(x) value is performedwhile the one or more GTs are adjusting its power output, that is, priorto a steady state operation. As described herein, this actual NO_(x)value can be measured (with updated measurements made) while the poweroutput is adjusted to approach the scaled power output value, and isused to iteratively refine operating condition(s) of the one or more GTsto move the GT(s) closer to a desired state. The scaled power outputvalue can be derived using a power scale factor, which can be a numbergreater than zero and less than or equal to one (1). That is, where eachGT's power output differs from the nominal power output by some value(e.g., ×MW), this process includes commanding each GT to adjust itspower output to match a value equal to a fraction of the differencebetween that GT's power output and the nominally output, e.g., 0.7×. Asnoted herein, this process will likely help to bring each GT's actualNOx value closer to the P50 NO_(x) value, but does not fully succeed inthat goal. Additionally, this power output adjustment does not addressanother concern, that being the elevated firing temperature relative toits desired level; and

3) Adjusting each GT's operating condition based upon its difference(Delta NO_(x)) between the measured actual NOx value (process 2) theexpected, P50 NO_(x) value for the ambient condition, and an emissionsscale factor (e.g., a value between zero and less than or equal to one(1), similar or distinct from the power scale factor). The Delta NO_(x)value can be translated to a Delta power output (MW) value (representingthe difference between the GT's actual power output and the power outputat the P50 power output level) for each GT using conventionalapproaches. In this process, each GT that deviates from the P50 poweroutput value, has its operating condition adjusted by a fixed fractionof the Delta power output value (as converted from the Delta NO_(x)value), adjusted by the emissions scale factor, such that it approaches(and reaches or approximately reaches) the Delta power output (MW) valuefor that GT. This adjustment will move each GT onto a line in Poweroutput/NO_(x) space that is nearly orthogonal to the P50 Poweroutput/P50 NO_(x) characteristic for that GT. The above-noted generalprocesses are described in further detail herein.

In the following description, reference is made to the accompanyingdrawings that form a part thereof, and in which is shown by way ofillustration specific example embodiments in which the present teachingsmay be practiced. These embodiments are described in sufficient detailto enable those skilled in the art to practice the present teachings andit is to be understood that other embodiments may be utilized and thatchanges may be made without departing from the scope of the presentteachings. The following description is, therefore, merely illustrative.

FIG. 1 shows a schematic illustration of a gas turbine engine (GT) 10including a control system 18, according to various embodiments. Invarious embodiments, gas turbine engine 10 includes a compressor 12, acombustor 14, a turbine 16 drivingly coupled to compressor 12, and acomputer control system, or controller 18. An inlet duct 20 tocompressor 12 channels ambient air and, in some instances, injectedwater to compressor 12. Duct 20 may include ducts, filters, screens, orsound absorbing devices that contribute to a pressure loss of ambientair flowing through inlet duct 20 and into inlet guide vanes (IGV) 21 ofcompressor 12. Combustion gasses from gas turbine engine 10 are directedthrough exhaust duct 22. Exhaust duct 22 may include sound adsorbingmaterials and emission control devices that induce a backpressure to gasturbine engine 10. An amount of inlet pressure losses and backpressuremay vary over time due to the addition of components to inlet duct 20and exhaust duct 22, and/or as a result of dust or dirt clogging inletduct 20 and exhaust duct 22, respectively. In various embodiments, gasturbine engine 10 drives a generator 24 that produces electrical power.

Various embodiments are described which measure, analyze and/or controla set of GTs, which may include one or more gas turbine engines (GTs),e.g., in a fleet. It is understood that these approaches are similarlyapplied to a single GT as two or more GTs. It is further understood thatthe term “set” as used herein can mean 1 or more.

In various embodiments, a plurality of control sensors 26 detect variousoperating conditions of gas turbine engine 10, generator 24, and/or theambient environment during operation of gas turbine engine 10. In manyinstances, multiple redundant control sensors 26 may measure the sameoperating condition. For example, groups of redundant temperaturecontrol sensors 26 may monitor ambient temperature, compressor dischargetemperature, turbine exhaust gas temperature, and/or other operatingtemperatures the gas stream (not shown) through gas turbine engine 10.Similarly, groups of other redundant pressure control sensors 26 maymonitor ambient pressure, static and dynamic pressure levels atcompressor 12, turbine 16 exhaust, and/or other parameters in gasturbine engine 10. Control sensors 26 may include, without limitation,flow sensors, pressure sensors, speed sensors, flame detector sensors,valve position sensors, guide vane angle sensors, and/or any otherdevice that may be used to sense various operating parameters duringoperation of gas turbine engine 10.

As used herein, the term “parameter” refers to characteristics that canbe used to define the operating conditions of gas turbine engine 10,such as temperatures, pressures, and/or gas flows at defined locationswithin gas turbine engine 10. Some parameters are measured, i.e., aresensed and are directly known, while other parameters are calculated bya model and are thus estimated and indirectly known. Some parameters maybe initially input by a user to controller 18. The measured, estimated,or user input parameters represent a given operating state of gasturbine engine 10.

A fuel control system 28 regulates an amount of fuel flow from a fuelsupply (not shown) to combustor 14, an amount split between primary andsecondary fuel nozzles (not shown), and an amount mixed with secondaryair flowing into combustor 14. Fuel control system 28 may also select atype of fuel for use in combustor 14. Fuel control system 28 may be aseparate unit or may be a component of controller 18.

Controller (control system) 18 may be a computer system that includes atleast one processor (not shown) and at least one memory device (notshown) that executes operations to control the operation of gas turbineengine 10 based at least partially on control sensor 26 inputs and oninstructions from human operators. The controller may include, forexample, a model of gas turbine engine 10. Operations executed bycontroller 18 may include sensing or modeling operating parameters,modeling operational boundaries, applying operational boundary models,or applying scheduling algorithms that control operation of gas turbineengine 10, such as by regulating a fuel flow to combustor 14. Controller18 compares operating parameters of gas turbine engine 10 to operationalboundary models, or scheduling algorithms used by gas turbine engine 10to generate control outputs, such as, without limitation, a firingtemperature. Commands generated by controller 18 may cause a fuelactuator 27 on gas turbine engine 10 to selectively regulate fuel flow,fuel splits, and/or a type of fuel channeled between the fuel supply andcombustors 14. Other commands may be generated to cause actuators 29 toadjust a relative position of IGVs 21, adjust inlet bleed heat, oractivate other control settings on gas turbine engine 10.

Operating parameters generally indicate the operating conditions of gasturbine engine 10, such as temperatures, pressures, and gas flows, atdefined locations in gas turbine engine 10 and at given operatingstates. Some operating parameters are measured, i.e., sensed and aredirectly known, while other operating parameters are estimated by amodel and are indirectly known. Operating parameters that are estimatedor modeled, may also be referred to as estimated operating parameters,and may include for example, without limitation, firing temperatureand/or exhaust temperature. Operational boundary models may be definedby one or more physical boundaries of gas turbine engine 10, and thusmay be representative of optimal conditions of gas turbine engine 10 ateach boundary. Further, operational boundary models may be independentof any other boundaries or operating conditions. Scheduling algorithmsmay be used to determine settings for the turbine control actuators 27,29 to cause gas turbine engine 10 to operate within predeterminedlimits. Typically, scheduling algorithms protect against worst-casescenarios and have built-in assumptions based on certain operatingstates. Boundary control is a process by which a controller, such ascontroller 18, is able to adjust turbine control actuators 27, 29 tocause gas turbine engine 10 to operate at a preferred state.

FIG. 2 shows a schematic view of an example control architecture 200that may be used with controller 18 (shown in FIG. 1) to controloperation of gas turbine engine 10 (shown in FIG. 1). More specifically,in various embodiments, control architecture 200 is implemented incontroller 18 and includes a model-based control (MBC) module 56. MBCmodule 56 is a robust, high fidelity, physics-based model of gas turbineengine 10. MBC module 56 receives measured conditions as input operatingparameters 48. Such parameters 48 may include, without limitation,ambient pressure and temperature, fuel flows and temperature, inletbleed heat, and/or generator power losses. MBC module 56 applies inputoperating parameters 48 to the gas turbine model to determine a nominalfiring temperature 50 (or nominal operating state 428). MBC module 56may be implemented in any platform that enables operation of controlarchitecture 200 and gas turbine engine 10 as described herein.

Further, in various embodiments, control architecture 200 includes anadaptive real-time engine simulation (ARES) module 58 that estimatescertain operating parameters of gas turbine engine 10. For example, inone embodiment, ARES module 58 estimates operational parameters that arenot directly sensed such as those generated by control sensors 26 foruse in control algorithms. ARES module 58 also estimates operationalparameters that are measured such that the estimated and measuredconditions can be compared. The comparison is used to automatically tuneARES module 58 without disrupting operation of gas turbine engine 10.

ARES module 58 receives input operating parameters 48 such as, withoutlimitation, ambient pressure and temperature, compressor inlet guidevane position, fuel flow, inlet bleed heat flow, generator power losses,inlet and exhaust duct pressure losses, and/or compressor inlettemperature. ARES module 58 then generates estimated operatingparameters 60, such as, without limitation, exhaust gas temperature 62,compressor discharge pressure, and/or compressor discharge temperature.In various embodiments, ARES module 58 uses estimated operatingparameters 60 in combination with input operating parameters 48 asinputs to the gas turbine model to generate outputs, such as, forexample, a calculated firing temperature 64.

In various embodiments, controller 18 receives as an input, a calculatedfiring temperature 52. Controller 18 uses a comparator 70 to comparecalculated firing temperature 52 to nominal firing temperature 50 togenerate a correction factor 54. Correction factor 54 is used to adjustnominal firing temperature 50 in MBC module 56 to generate a correctedfiring temperature 66. Controller 18 uses a comparator 74 to compare thecontrol outputs from ARES module 58 and the control outputs from MBCmodule 56 to generate a difference value. This difference value is theninput into a Kalman filter gain matrix (not shown) to generatenormalized correction factors that are supplied to controller 18 for usein continually tuning the control model of ARES module 58 thusfacilitating enhanced control of gas turbine engine 10. In analternative embodiment, controller 18 receives as an input exhausttemperature correction factor 68. Exhaust temperature correction factor68 may be used to adjust exhaust temperature 62 in ARES module 58.

FIG. 3 is a graph that shows a probabilistic simulation of the operatingstates of a statistically significant number of the gas turbine engine10 of FIG. 1 using the model of gas turbine engine used by controller18. The graph represents power output versus firing temperature of gasturbine engine 10. Line 300 is the linear regression model for theplurality of data points 308. Lines 302 represent the 99% predictioninterval corresponding to data points 308. Further, line 304 representsthe nominal or design firing temperature 50 for gas turbine engine 10,and line 306 represents a nominal or design power output for gas turbineengine 10. In various embodiments, the probabilistic simulation shown inFIG. 3 shows an approximate variance in firing temperature of 80 units.This variance may be attributed to the component tolerances of gasturbine engine 10, and the measurement uncertainty of controller 18 andcontrol sensors 26.

Described herein are approaches for tuning gas turbine engine 10 thatfacilitates reducing variation in the actual gas turbine engine 10operating state, e.g., firing temperature and/or exhaust temperature,which facilitates reducing variation in power output, emissions, andlife of gas turbine engine 10. The probabilistic control approachesdescribed herein may be implemented as either a discrete process to tunegas turbine engine 10 during installation and at various periods, or maybe implemented within controller 18 to run periodically at apredetermined interval and/or continuously during operation of gasturbine engine 10. These approaches do not measure gas turbine firingtemperature directly because firing temperature is an estimatedparameter, as previously discussed. These probabilistic controlapproaches, however, can yield directly measured parameters that arestrong indicators of the firing temperature of the gas turbine engine10, and allow for improved control over the firing temperature in a gasturbine engine 10.

FIG. 4 shows a flow diagram illustrating a method performed according tovarious embodiments. As described herein, the method can be performed(e.g., executed) using at least one computing device, implemented as acomputer program product (e.g., a non-transitory computer programproduct), or otherwise include the following processes:

Process P1: modeling each GT 10 in the set of GTs at a base load level(e.g., target indicated firing temperature), based upon a measuredambient condition for each GT 10. The modelling process can includeobtaining a model representative of the one or more gas turbines (e.g.,a statistical model, a measurement-based model, etc.), and using themeasured ambient condition as an input, simulating (modeling) thebehavior of the gas turbine(s) as it (they) approach the designed baseload. As noted herein, the base load (with a target indicated firingtemp) is associated with a power output (MW) value and an emissionsvalue for the measured ambient condition. As further noted herein, inresponse to modeling each GT 10 in the set of GTs at the base loadlevel, each GT 10 does not attain at least one of the nominal poweroutput value (P50 Power output) or the nominal emissions value (P50NO_(x)), and as such, models real-world behavior. According to variousembodiments, the process of modeling each GT 10 in the set of GTs to arespective power output to match the nominal power output value moves anactual emissions value for each GT 10 close to the nominal emissionsvalue without matching the nominal emissions value;

Process P2: commanding each GT 10 in the set of GTs to adjust arespective power output to match a scaled power output value equal to afraction of a difference between the respective power output and thenominal power output (P50 power output) value, and during the adjustingof the respective power output, measuring the actual emissions value foreach GT 10. Measuring the actual emissions value is performed while eachGT 10 is adjusting its power output (e.g., as a snapshot, incrementally,or iteratively), that is, prior to a steady state operation. Asdescribed herein, this actual emissions value can be measured (withupdated measurements made) while the power output of each GT 10 isadjusted to approach the scaled power output value, and is used toiteratively refine operating condition(s) of each GT 10 to move the GT10 closer to a desired state (as described with respect to process P3).The scaled power output value can be derived using a power scale factor,which can be a number greater than zero and less than one (1). That is,where each GT's power output differs from the nominal power output bysome value (e.g., ×MW), this process includes commanding each GT toadjust its power output to match a value equal to a fraction of thedifference between that GT's power output and the nominally output,e.g., 0.6× or 0.7×. The power scale factor (S_(MW)) can be created usingone or more modeling processes to predict how a fleet of GTs 10 willperform when operated at distinct MW/NO_(x) conditions. In variousembodiments, the power scale factor can be derived using iterativetesting and/or modeling of particular GTs 10 under a variety ofconditions. In some cases, the power scale factor (S_(MW)) is selectedbased upon a desired standard deviation for a fleet of GTs 10, e.g.,based upon one or more models, the power scale factor indicates that theGTs 10 will remain within some standard deviation band of the nominalGT. In various embodiments, process P2 can further include convertingthe difference between the respective measured actual emissions value(measured during transient state, in some cases, repeatedly) and thenominal emissions value for each GT 10 into a difference between arespective power output value and the nominal power output value at theambient condition value for each GT 10; and

Process P3: adjusting an operating condition of each GT 10 in the set ofGTs based upon a difference between the respective measured actualemissions value, a nominal emissions value at the ambient condition andan emissions scale factor (e.g., a value between zero and one (1),similar or distinct from the power scale factor). According to variousembodiments, the process of adjusting the operating condition of each GT10 includes adjusting the operating condition of each GT 10 in the setof GTs by a fixed fraction of the difference between the respectivepower output value and the nominal power output value, adjusted by theemissions scale factor, such that the power output of each GT 10approaches (and in some cases reaches or approximately reaches) arespective nominal power output value. According to various embodiments,adjusting of the operating condition of each GT 10 in the set of GTs bythe fixed fraction of the difference between the respective power outputvalue and the nominal power output value, adjusted by the emissionsscale factor (e.g., 0.7, 0.8, 0.9), aligns each GT 10 on a line ingraphical space plotting power output versus emissions that isorthogonal to a nominal power output/nominal emissions characteristicfor each GT 10.

It is understood that the adjustment of the operating condition of eachGT 10 as described with respect to process P3 can be performediteratively (e.g., more than one time) based upon updated measurement ofthe actual emissions value of the corresponding GT 10 obtained duringprocess P2. That is, using transient state (e.g., not steady state)measurements allows for an iterative modification of the operatingcondition in order to move each GT 10 closer to its target location inMW/NO_(x) space.

FIGS. 5-7 show graphical depictions, via Power output v. Emissions(NO_(x)) graphs, of the processes described in FIG. 4, with respect toan example data set representing a set (plurality) of GTs (similar to GT10). All data points shown in FIGS. 5-6 represent Power output v.Emissions (NO_(x)) at indicated firing temperatures, where “indicated”firing temperature is the firing temperature as displayed or otherwiseoutputted by the controller of GT 10. That is, the “indicated” firingtemperature is not necessarily the actual firing temperature (which, asdescribed herein, cannot be accurately measured), but instead, thefiring temperature as estimated by the controller (and relatedequipment) of the GT 10.

As shown in this example, e.g., in FIG. 5, the center point of line GLis a function of the mean firing temperature (T4) of the set of GTs. Themean combustion temperature (T3.9) is a function of the mean firingtemperature, and is greater than the mean firing temperature. Notedherein, as the mean firing temperature increases, so will the meancombustion temperature, meaning that line GL will shift to a greaterPower output/NO_(x) value, while remaining orthogonal to line RL, whichdefines the Power output/NO_(x) characteristic for the mean GT in theset at base load. The two lines labeled BL bound line GL, and define thestatistical variation among the set of GTs, to two sigma (Σ), from themean line RL. The inventors have discovered through empirical testingthat lines BL represent a +/−10 degree span in actual firing temperature(T4) from line RL, as measured along a given line orthogonal to line RL.FIG. 6 shows the graphical depiction of FIG. 5, with the addition ofindicators for the Mean T4 (firing temperature) at distinct examplePower output/NO_(x) values for a fleet of GTs, along lines orthogonal toRL (Power output/NO_(x) characteristic) and lines BL. Mean T4 (B) andMean T4 (P) in this example, illustrate example fleets at T4=2,410degrees F. and T4=2,430 degrees F., respectively. FIG. 6 alsoillustrates a line PL, which is an example of a single GT along a firingtemperature (T4) “sweep” or variation orthogonal with the Poweroutput/NOx characteristic line. PL shows how the Power output/NOx variesby a changing firing temperature (T4).

FIG. 7 shows a three-dimensional graphical depiction of the process P3(FIG. 4), namely, adjusting an operating condition of each GT in the setof GTs based upon a difference between the respective measured actualemissions value and a nominal emissions value at the ambient condition.That is, as shown in FIG. 7, the GL plane, defined by the plane of theGL (FIGS. 5-6) across firing temperature (T4) space (scaled according tothe applied emissions scale factor), illustrates a model of where theset of GTs operate in the firing temperature (T4) space. That is,although actual firing temperature (T4) cannot be directly measured foreach GT in the set of GTs, the GL plane represents the most accuratemodel of the firing temperature of GTs within the set of GTs. Accordingto the various embodiments, process P3 includes adjusting an operatingcondition of each GT based upon a difference between its respectivemeasured actual emissions value (NO_(x) value) and a nominal (average)emissions value (NO_(x) value) for the respective GT, at an emissionsscale factor. That is, according to various embodiments, an operatingcondition of each GT is adjusted such that its Power output/NO_(x) valueintersects GL in two-dimensional space (FIGS. 5-6), and the GL plane inthree-dimensional space (FIG. 7). The intersection of the nominal (P50)Power output/NOx lines and the GL plane represents the most accuratemodel of the desired mean actual firing temperature (P4), and by tuningeach GT 10 to approach that GL plane, firing temperature variation isreduced across the fleet, increasing the life of the fleet.

The GL (and the GL plane) is a characteristic of how gas turbines aredesigned and built, and in Power output/NO_(x) space, its center is atthe intersection of P50 Power output and P50 NO_(x) for the particulartype of GT 10 in a fleet. The length of GL in two-dimensional space(e.g., the space between BLs, FIGS. 5-6)) is defined by the GT-to-GThardware variation for a given type of GT (e.g., physical variances inthe manufacture of two machines to the same specifications). By alteringoperating conditions of a GT 10 in order to align the Poweroutput/NO_(x) value for that GT 10 with the GL (and GL plane), thevariation in the actual firing temperature (T4) is minimized.

According to various embodiments, the graphical depictions shown inFIGS. 5-7 can be derived from Equations 1-4, which provide solutions forthe change in operating state (ΔOperatingState) of GT 10, as well as thechange in actual firing temperature (ΔT₄). As shown, Equations 1-4 areas follows:ΔOperatingState=ΔMW_(Step1-2)+ΔNOx_(Step2-3)ΔT _(4,Step1-3) =ΔT _(4,Step1-2) +ΔT _(4,Step2-3)ΔT _(4,Step1-2) =fn(ΔMW_(Step1-2))=fn(S _(MW)*(MW_(P50)−MW₁))ΔT _(4,Step2-3) =fn(ΔNOx_(Step2-3))=fn(S _(NOx)*(NOx₃−NOx₂))

Where Step 1=process P1; Step 2=process P2; Step 3=process P3;Variable1=a first performance variable that can be measured from anexternal sensor on GT 10 (e.g., mega-watt output); Variable2=a second(distinct from Variable1, but not independent) performance variable(e.g., emissions) that can be measured from an external sensor on GT 10(e.g., an exhaust temperature, exhaust gas flow, etc.);S_(V1)=S_(MW)=scale factor for Variable1 (e.g., MW scale factor);S_(V2)=S_(NOx)=scale factor for Variable2 (e.g., NO_(x) scale factor).As shown in Table 1 below, example scale factors can be chosen accordingto various embodiments to manipulate actual firing temperature,emissions, mega-watt output, etc. As noted herein, the terms “step 1,”“step 2,” and “step 3” can be used to refer to processes P1, P2 and P3,respectively.

TABLE 1 Effect of Scale Factor (steps or processes P1/S1; P2/S2; P3/S3)S3 Scale 0 X X + Y X + CY X + 2CY X + 3CY X + 4CY S2 Scale 0 S1 Only S2Scale Y S2 Scale Y + X 2Scale S2 Scale Y + CX Min Mw Balanced BalancedBalanced ~T4 Min NOx Min (S2 only) Variation Variation Variation (S3)(S3) (S3) S2 Scale Y + 2CX Min Mw Balanced Balanced Balanced ~T4 Min NOxMin (S2 only) Variation Variation Variation (S3) (S3) (S3)

As is evident in the example scale factors in Table 1, scale factors forMW (step 2, or process P2) and NO_(x) (step 3, or process P3) can beselected according to empirical and/or model-based data to enhance thedesired outcome for a particular GT 10 or fleet of GTs 10. For example,where the objective is to minimize variation in either MW or NO_(x),scale factors may be chosen such that the “min MW” or “NO_(x) min”intersection is selected. Moving from the “min MW” box to the right(increasing NOx scale factor) trades variation in MW and fuel forvariation in NOx and T4. The band labeled “balanced variation”represents a minimum region in the four-dimensional MW/NO_(x)/T4/FuelSpace (FIG. 7). For one GT 10, there is a minimum in T4 variation at aNOx scale factor of 0.875. The value at which such a minimum occurs is afunction of the NO_(x) v. T4 characteristic of the GT's combustor (e.g.,a dry low NO_(x) combustor). In the case where two scale factors areapplied (MW scale factor and NO scale factor), a MW scale factor of 0.4provides variation which may be substantially equivalent to previouslydisclosed (unscaled) approaches. However, as can be seen in this exampleTable, a combination of 0.7 as MW scale factor and 0.925 as NO scalefactor provides a minimum variation in T4 for the fleet of GTs 10.

According to various embodiments, Equations 1-4 noted herein can bemodified in order to describe iteratively performing processes P1-P3,based upon the difference between the measured transient emissions(Process P2) and the nominal emissions value at the ambient condition.That is, as each GT 10 approaches its target power output value and/oremissions value, the difference between its current value (transient MWand/or transient NON value) and the target value (e.g., P50 MW, P50 NON)can be represented in equation form as an Error Signal (e.g.,ErrorSignal=DesiredState₃−CurrentState₁, where numerical indicatorscorrespond with Processes P1-P3). As the error signal approaches zero,the difference between the actual GT 10 operating conditions and itstarget operating conditions decreases (where zero error signal isdesired).

FIG. 8 shows an illustrative graphical depiction of an exampleprogression in Error Signal v. MW for a GT (e.g., GT 10) undergoingprocesses P1-P3 as described herein. The data point highlighted by P1indicates a higher error signal associated with a lower (relative) poweroutput (MW), and the data point highlighted by P2 indicates somecorrection in that high error signal, made at least in part byincreasing the power output (MW). However, in this example, process P2increases the power output of the GT 10 too significantly, resulting ina relatively high negative error signal value. The transition from datapoint P2 to data point P3, however, highlights various features of thedisclosure which allow for progressive movement from the undesirable(e.g., low or high) error signal toward a zero (0) error signal. Thatis, using the transient emissions values obtained in process P2described herein, the error signal for GT 10 is incrementally reduceduntil reaching a desired level (e.g., within an acceptable tolerance of0.0). It is understood that this example depiction in FIG. 8 illustratesone way in which a GT 10 may be adjusted to minimize its associatederror signal, and that other adjustments (e.g., adjustments from largernegative error signals, to smaller positive signals, to even smallernegative error signals) are possible according to various aspects of thedisclosure. It is understood that the graphical depiction in FIG. 8could be similarly shown for Error Signal v. Emissions (NO_(x)) to showthe incremental adjustment of a GT 10 from processes P2 to P3, utilizingtransient measurements, as described according to various embodimentsherein.

FIG. 9 shows an illustrative environment 802 demonstrating thecontroller (control system 18) coupled with the GTs 10 via at least onecomputing device 814. As described herein, the control system 18 caninclude any conventional control system components used in controlling agas turbine engine (GT). For example, the control system 18 can includeelectrical and/or electro-mechanical components for actuating one ormore components in the GT(s) 10. The control system 18 can includeconventional computerized sub-components such as a processor, memory,input/output, bus, etc. The control system 18 can be configured (e.g.,programmed) to perform functions based upon operating conditions from anexternal source (e.g., at least one computing device 814), and/or mayinclude pre-programmed (encoded) instructions based upon parameters ofthe GT(s) 10.

The system 802 can also include at least one computing device 814connected (e.g., hard-wired and/or wirelessly) with the control system18 and GT(s) 10. In various embodiments, the computing device 814 isoperably connected with the GT(s) 10, e.g., via a plurality ofconventional sensors such as flow meters, temperature sensors, etc., asdescribed herein. The computing device 814 can be communicativelyconnected with the control system 18, e.g., via conventional hard-wiredand/or wireless means. The control system 18 is configured to monitorthe GT(s) 10 during operation according to various embodiments.

Further, computing device 814 is shown in communication with a user 836.A user 836 may be, for example, a programmer or operator. Interactionsbetween these components and computing device 814 are discussedelsewhere in this application.

As noted herein, one or more of the processes described herein can beperformed, e.g., by at least one computing device, such as computingdevice 814, as described herein. In other cases, one or more of theseprocesses can be performed according to a computer-implemented method.In still other embodiments, one or more of these processes can beperformed by executing computer program code (e.g., control system 18)on at least one computing device (e.g., computing device 814), causingthe at least one computing device to perform a process, e.g., tuning atleast one GT 10 according to approaches described herein.

In further detail, computing device 814 is shown including a processingcomponent 122 (e.g., one or more processors), a storage component 124(e.g., a storage hierarchy), an input/output (I/O) component 126 (e.g.,one or more I/O interfaces and/or devices), and a communications pathway128. In one embodiment, processing component 122 executes program code,such as control system 18, which is at least partially embodied instorage component 124. While executing program code, processingcomponent 122 can process data, which can result in reading and/orwriting the data to/from storage component 124 and/or I/O component 126for further processing. Pathway 128 provides a communications linkbetween each of the components in computing device 814. I/O component126 can comprise one or more human I/O devices or storage devices, whichenable user 836 to interact with computing device 814 and/or one or morecommunications devices to enable user 136 and/or CS 138 to communicatewith computing device 814 using any type of communications link. To thisextent, control system 18 can manage a set of interfaces (e.g.,graphical user interface(s), application program interface, and/or thelike) that enable human and/or system interaction with control system18.

In any event, computing device 814 can comprise one or more generalpurpose computing articles of manufacture (e.g., computing devices)capable of executing program code installed thereon. As used herein, itis understood that “program code” means any collection of instructions,in any language, code or notation, that cause a computing device havingan information processing capability to perform a particular functioneither directly or after any combination of the following: (a)conversion to another language, code or notation; (b) reproduction in adifferent material form; and/or (c) decompression. To this extent,control system 18 can be embodied as any combination of system softwareand/or application software. In any event, the technical effect ofcomputing device 814 is to tune at least one GT 10 according to variousembodiments herein.

Further, control system can be implemented using a set of modules 132.In this case, a module 132 can enable computing device 814 to perform aset of tasks used by control system 18, and can be separately developedand/or implemented apart from other portions of control system 18.Control system 18 may include modules 132 which comprise a specific usemachine/hardware and/or software. Regardless, it is understood that twoor more modules, and/or systems may share some/all of their respectivehardware and/or software. Further, it is understood that some of thefunctionality discussed herein may not be implemented or additionalfunctionality may be included as part of computing device 814.

When computing device 814 comprises multiple computing devices, eachcomputing device may have only a portion of control system 18 embodiedthereon (e.g., one or more modules 132). However, it is understood thatcomputing device 814 and control system 18 are only representative ofvarious possible equivalent computer systems that may perform a processdescribed herein. To this extent, in other embodiments, thefunctionality provided by computing device 814 and control system 18 canbe at least partially implemented by one or more computing devices thatinclude any combination of general and/or specific purpose hardware withor without program code. In each embodiment, the hardware and programcode, if included, can be created using standard engineering andprogramming techniques, respectively.

Regardless, when computing device 814 includes multiple computingdevices, the computing devices can communicate over any type ofcommunications link. Further, while performing a process describedherein, computing device 814 can communicate with one or more othercomputer systems using any type of communications link. In either case,the communications link can comprise any combination of various types ofwired and/or wireless links; comprise any combination of one or moretypes of networks; and/or utilize any combination of various types oftransmission techniques and protocols.

As discussed herein, control system 18 enables computing device 814 tocontrol and/or tune at least one GT 10. Control system 18 may includelogic for performing one or more actions described herein. In oneembodiment, control system 18 may include logic to perform theabove-stated functions. Structurally, the logic may take any of avariety of forms such as a field programmable gate array (FPGA), amicroprocessor, a digital signal processor, an application specificintegrated circuit (ASIC) or any other specific use machine structurecapable of carrying out the functions described herein. Logic may takeany of a variety of forms, such as software and/or hardware. However,for illustrative purposes, control system 18 and logic included thereinwill be described herein as a specific use machine. As will beunderstood from the description, while logic is illustrated as includingeach of the above-stated functions, not all of the functions arenecessary according to the teachings of the invention as recited in theappended claims.

In various embodiments, control system 18 may be configured to monitoroperating parameters of one or more GT(s) 10 as described herein.Additionally, control system 18 is configured to command the one or moreGT(s) 10 to modify those operating parameters in order to achieve thecontrol and/or tuning functions described herein.

It is understood that in the flow diagram shown and described herein,other processes may be performed while not being shown, and the order ofprocesses can be rearranged according to various embodiments.Additionally, intermediate processes may be performed between one ormore described processes. The flow of processes shown and describedherein is not to be construed as limiting of the various embodiments.

In any case, the technical effect of the various embodiments of theinvention, including, e.g., the control system 18, is to control and/ortune one or more GT(s) 10 as described herein.

In various embodiments, components described as being “coupled” to oneanother can be joined along one or more interfaces. In some embodiments,these interfaces can include junctions between distinct components, andin other cases, these interfaces can include a solidly and/or integrallyformed interconnection. That is, in some cases, components that are“coupled” to one another can be simultaneously formed to define a singlecontinuous member. However, in other embodiments, these coupledcomponents can be formed as separate members and be subsequently joinedthrough known processes (e.g., fastening, ultrasonic welding, bonding).

When an element or layer is referred to as being “on”, “engaged to”,“connected to” or “coupled to” another element or layer, it may bedirectly on, engaged, connected or coupled to the other element orlayer, or intervening elements or layers may be present. In contrast,when an element is referred to as being “directly on,” “directly engagedto”, “directly connected to” or “directly coupled to” another element orlayer, there may be no intervening elements or layers present. Otherwords used to describe the relationship between elements should beinterpreted in a like fashion (e.g., “between” versus “directlybetween,” “adjacent” versus “directly adjacent,” etc.). As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

We claim:
 1. A computing system comprising: at least one computingdevice having at least one controller, the at least one computing deviceconfigured to tune each gas turbine in a set of a plurality of gasturbines based upon a power output parameter and an emissions parameter,wherein the at least one computing device is configured to: model eachgas turbine in the set at a respective base load level based upon arespective measured ambient condition; command each gas turbine in theset to adjust a respective actual value of the power output parameter ofeach gas turbine in the set to match a respective scaled value of thepower output parameter of each gas turbine in the set, wherein therespective scaled value of the power output parameter is equal to afraction of a difference between the respective actual value of thepower output parameter and a nominal value of the power output parameterof the set, and measure a respective actual value of the emissionsparameter for each gas turbine in the set during the adjustment of therespective actual value of the power output parameter; and perform anadjustment to a respective operating parameter of each gas turbine inthe set based upon a respective scaled value of the emissions parameterof each gas turbine in the set, wherein the respective scaled value ofthe emissions parameter is equal to a product of an emissions scalefactor multiplied by a difference between the respective actual value ofthe emissions parameter and a nominal value of the emissions parameterat the respective measured ambient condition for each gas turbine in theset, wherein the adjustment to the respective operating parameter ofeach gas turbine in the set aligns each gas turbine in the set onto afirst line in a graphical space plotting the power output parameterversus the emissions parameter, wherein the first line is orthogonal toa characteristic line in the graphical space, wherein the characteristicline is a mean characteristic line of all of the plurality of gasturbines in the set, at the respective base load level of each gasturbine in the set, plotting the power output parameter versus theemissions parameter.
 2. The computing system of claim 1, wherein therespective base load level is associated with a base load value of thepower output parameter and a base load value of the emissions parameterfor the respective measured ambient condition, and wherein the fractionis a power scale factor.
 3. The computing system of claim 1, wherein inresponse to the modeling of each gas turbine in the set at therespective base load level, each gas turbine in the set does not attainat least one of: the nominal value of the power output parameter of theset and the nominal value of the emissions parameter at the respectivemeasured ambient condition.
 4. The computing system of claim 1, whereinthe at least one computing device is further configured to convert thedifference between the respective actual value of the emissionsparameter and the nominal value of the emissions parameter at therespective measured ambient condition for each gas turbine in the setinto a difference between a respective value of the power outputparameter along the first line and the nominal value of the power outputparameter of the set for each gas turbine in the set.
 5. The computingsystem of claim 4, wherein the adjustment to the respective operatingparameter of each gas turbine in the set includes adjusting theoperating parameter of each gas turbine in the set by a fraction of thedifference between the respective value of the power output parameteralong the first line and the nominal value of the power output parameterof the set, such that the power output parameter of each gas turbine inthe set approaches and then reaches a respective nominal value of thepower output parameter along the first line.
 6. The computing system ofclaim 1, wherein the commanding of each gas turbine in the set to adjustthe respective actual value of the power output parameter of each gasturbine in the set to match the respective scaled value of the poweroutput parameter moves the emissions parameter for each gas turbine inthe set closer to the nominal value of the emissions parameter at therespective measured ambient condition without matching the nominal valueof the emissions parameter at the respective measured ambient condition.7. A computer program product comprising program code embodied in atleast one non-transitory computer readable medium, which when executedby at least one computing device having at least one controller, causesthe at least one computing device to tune each gas turbine in a set of aplurality of gas turbines based upon a power output parameter and anemissions parameter by: modelling each gas turbine in the set at arespective base load level based upon a respective measured ambientcondition; commanding each gas turbine in the set to adjust a respectiveactual value of the power output parameter of each gas turbine in theset to match a respective scaled value of the power output parameter ofeach gas turbine in the set, wherein the respective scaled value of thepower output parameter is equal to a fraction of a difference betweenthe respective actual value of the power output parameter and a nominalvalue of the power output parameter of the set, and measuring arespective actual value of the emissions parameter for each gas turbinein the set during the adjusting of the respective actual value of thepower output parameter; and adjusting a respective operating parameterof each gas turbine in the set based upon a respective scaled value ofthe emissions parameter of each gas turbine in the set, wherein therespective scaled value of the emissions parameter is equal to a productof an emissions scale factor multiplied by a difference between therespective actual value of the emissions parameter and a nominal valueof the emissions parameter at the respective measured ambient conditionfor each gas turbine in the set, wherein the adjusting of the respectiveoperating parameter of each gas turbine in the set aligns each gasturbine in the set onto a first line in a graphical space plotting thepower output parameter versus the emissions parameter, wherein the firstline is orthogonal to a characteristic line in the graphical space,wherein the characteristic line is a mean characteristic line of all ofthe plurality of gas turbines in the set, at the respective base loadlevel of each gas turbine in the set, plotting the power outputparameter versus the emissions parameter.
 8. The computer programproduct of claim 7, wherein the respective base load level is associatedwith a base load value of the power output parameter and a base loadvalue of the emissions parameter for the respective measured ambientcondition, and wherein the fraction is a power scale factor.
 9. Thecomputer program product of claim 7, wherein in response to the modelingof each gas turbine in the set at the respective base load level, eachgas turbine in the set does not attain at least one of: the nominalvalue of the power output parameter of the set and the nominal value ofthe emissions parameter at the respective measured ambient condition.10. The computer program product of claim 7, which when executed, causesthe at least one computing device to convert the difference between therespective actual value of the emissions parameter and the nominal valueof the emissions parameter at the respective measured ambient conditionfor each gas turbine in the set into a difference between a respectivevalue of the power output parameter along the first line and the nominalvalue of the power output parameter of the set for each gas turbine inthe set.
 11. The computer program product of claim 10, wherein theadjusting of the respective operating parameter of each gas turbine inthe set includes adjusting the operating parameter of each gas turbinein the set by a fraction of the difference between the respective valueof the power output parameter along the first line and the nominal valueof the power output parameter of the set, such that the power outputparameter of each gas turbine in the set approaches and then reaches arespective nominal value of the power output parameter along the firstline.
 12. The computer program product of claim 7, wherein thecommanding of each gas turbine in the set to adjust the respectiveactual value of the power output parameter of each gas turbine in theset to match the respective scaled value of the power output parametermoves the emissions parameter for each gas turbine in the set closer tothe nominal value of the emissions parameter at the respective measuredambient condition without matching the nominal value of the emissionsparameter at the respective measured ambient condition.
 13. Acomputer-implemented method of tuning each gas turbine in a set of aplurality of gas turbines based upon a power output parameter and anemissions parameter, performed using at least one computing devicehaving at least one controller, the computer-implemented methodcomprising: modelling each gas turbine in the set at a respective baseload level based upon a respective measured ambient condition;commanding each gas turbine in the set to adjust a respective actualvalue of the power output parameter of each gas turbine in the set tomatch a respective scaled value of the power output parameter of eachgas turbine in the set, wherein the respective scaled value of the poweroutput parameter is equal to a fraction of a difference between therespective actual value of the power output parameter and a nominalvalue of the power output parameter of the set, and measuring arespective actual value of the emissions parameter for each gas turbinein the set during the adjusting of the respective actual value of thepower output parameter; and adjusting a respective operating parameterof each gas turbine in the set based upon a respective scaled value ofthe emissions parameter of each gas turbine in the set, wherein therespective scaled value of the emissions parameter is equal to a productof an emissions scale factor multiplied by a difference between therespective actual value of the emissions parameter and a nominal valueof the emissions parameter at the respective measured ambient conditionfor each gas turbine in the set, wherein the adjusting of the respectiveoperating parameter of each gas turbine in the set aligns each gasturbine in the set onto a first line in a graphical space plotting thepower output parameter versus the emissions parameter, wherein the firstline is orthogonal to a characteristic line in the graphical space,wherein the characteristic line is a mean characteristic line of all ofthe plurality of gas turbines in the set, at the respective base loadlevel of each gas turbine in the set, plotting the power outputparameter versus the emissions parameter.
 14. The computer-implementedmethod of claim 13, wherein the respective base load level is associatedwith a base load value of the power output parameter and a base loadvalue of the emissions parameter for the respective measured ambientcondition, and wherein the fraction is a power scale factor.
 15. Thecomputer-implemented method of claim 14, wherein in response to themodeling of each gas turbine in the set at the respective base loadlevel, each gas turbine in the set does not attain at least one of: thenominal value of the power output parameter of the set and the nominalvalue of the emissions parameter at the respective measured ambientcondition.
 16. The computer-implemented method of claim 15, furthercomprising converting the difference between the respective actual valueof the emissions parameter and the nominal value of the emissionsparameter at the respective measured ambient condition for each gasturbine in the set into a difference between a respective value of thepower output parameter along the first line and the nominal value of thepower output parameter of the set for each gas turbine in the set. 17.The computer-implemented method of claim 16, wherein the adjusting ofthe respective operating parameter of each gas turbine in the setincludes adjusting the operating parameter of each gas turbine in theset by a fraction of the difference between the respective value of thepower output parameter along the first line and the nominal value of thepower output parameter of the set, such that the power output parameterof each gas turbine in the set approaches and then reaches a respectivenominal value of the power output parameter along the first line. 18.The computer-implemented method of claim 13, wherein the commanding ofeach gas turbine in the set to adjust the respective actual value of thepower output parameter of each gas turbine in the set to match therespective scaled value of the power output parameter moves theemissions parameter for each gas turbine in the set closer to thenominal value of the emissions parameter at the respective measuredambient condition without matching the nominal value of the emissionsparameter at the respective measured ambient condition.